Guided Ultrasonic Wave Imaging for Immersed Plates Based on Wavelet Transform and Probabilistic Analysis

Bagheri, Abdollah; Pistone, Elisabetta; Rizzo, Piervincenzo

doi:10.1080/09349847.2013.837212

Cited by 6 publications

(6 citation statements)

References 37 publications

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“…SW analysis may be more important even than other popular methods such as refraction or resistivity surveys or magnetic imaging (Crice, 2005). SW can characterize a medium at large (e.g., in regional and global seismology, to determine the structure of the earth's crust and upper mantle), intermediate (e.g., in exploration geophysics, to constrain near-surface structure, or to correct statics; or in geotechnical engineering, to infer the shear stiffness of the ground materials), and even at the smallest scales, for the nondestructive evaluation of engineering materials (e.g., using ultrasonic SWs to detect material defects; Thompson and Chimenti, 1997;Bagheri et al, 2014).…”

Section: Surface Waves: Applicationsmentioning

confidence: 99%

Multiscale adjoint waveform tomography for surface and body waves

2015

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International audienceWe have developed a wavelet-multiscale adjoint scheme for the elastic full-waveform inversion of seismic data, including body waves (BWs) and surface waves (SWs). We start the inversion on the SW portion of the seismograms. To avoid cycle skipping and reduce the dependence on the initial model of these dispersive waves, we commence by minimizing an envelope-based misfit function. Subsequently, we proceed to the minimization of a waveform-difference (WD) metric applied to the SWs only. After that, we fit BWs and SWs indiscriminately using a WD misfit metric. In each of these three steps, we guide the iterative inversion through a sequence of nested subspace projections in a wavelet basis. SW analysis preserves a wealth of near-surface features that would be lost in conventional BW tomography. We used a toy model to illustrate the dispersive and cycle-skipping behavior of the SWs, and to introduce the two ways by which we combat the nonlinearity of waveform inversions involving SWs. The first is the wavelet-based multiscale character of the method, and the second the envelope-based misfit function. Next, we used an industry synthetic model to perform realistic numerical experiments to further develop a strategy for SW and joint SW as well as BW tomography. The effect of incorrect density information on wave-speed inversions was also evaluated. We ultimately formalize a flexible scheme for full-waveform inversion based on adjoint methods that includes BWs and SWs, and also considers P- and S-wave speeds, as well as density. Our method is applicable to waveform inversion in exploration geophysics, geotechnical engineering, regional, and global seismology.Read More: http://library.seg.org/doi/10.1190/geo2014-0461.

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Section: Surface Waves: Applicationsmentioning

confidence: 99%

Multiscale adjoint waveform tomography for surface and body waves

2015

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“…In the CWT, a wavelet,

, is the analyzing function. This method compares the signal with the shift and dilation of a wavelet [ 35 , 36 , 37 , 38 , 39 , 40 ]. Here, dilation refers to stretched or compressed versions of the wavelet.…”

Section: Resultsmentioning

confidence: 99%

Numerical Analysis of Guided Waves to Improve Damage Detection and Localization in Multilayered CFRP Panel

et al. 2022

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Multilayered carbon fiber-reinforced polymers (CFRP) are increasingly used in aircraft components because of their superior mechanical properties. However, composite materials are vulnerable to impact loads, resulting in delamination-type damage which, if unnoticed, could lead to catastrophic structural failure. The objective of this research was to investigate possibilities to improve damage detection and the localization using signal processing methods. Numerical modeling using the semi-analytical finite element (SAFE) method was performed to obtain guided wave dispersion curves and to perform modal analysis. From the modal analysis, mode for inspection of the composite with delamination type defects was selected. From the numerical simulation, mode interaction with delamination along the longitudinal direction was analyzed and the location of the defect was estimated by measuring the time of flight (ToF) of the signal using Hilbert transform (HT) and continuous wavelet transform (CWT). The CWT has shown better results in estimating the delamination location compared with HT. The depth of delamination was characterized in the frequency domain by comparing the amplitude of the mode. Inverse fast Fourier transform (IFFT) is recommended to reconstruct the reflected and transmitted modes for better damage detection and to reduce the complexity of signal interpretation.

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“…4 show the time waveforms recorded by the five sensors during the first scan at the testing point 5. The wave packet tailing the leaky S 0 mode is the leaky A 0 mode, followed by the quasi-Scholte wave 26,29,30 propagating at the solid-liquid interface, and the leaky S 0 mode reflected from the water surface. To ease the direct comparison among the ultrasonic traces, the scale of the amplitudes was kept identical.…”

Section: Feature Extractionmentioning

confidence: 99%

“…[26][27][28][29] In the study presented in this article, we replaced the laser with a focused transducer to generate tone bursts centered at 500 kHz. The system consists of a pulsed laser or a focused immersion transducer to insonify the waveguide and an array of immersion ultrasonic transducers (IUTs) to sense leaky Lamb waves.…”

Section: Introductionmentioning

confidence: 99%

Guided ultrasonic wave testing of an immersed plate with hidden defects

Bagheri

Rizzo

2015

Opt. Eng

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This paper presents the results of an experimental study in which guided ultrasonic waves are used for the contactless nondestructive testing of a plate immersed in water. In the experiment, narrowband leaky Lamb waves are generated using a focused transducer and are detected with an array of five immersion sensors arranged in a semicircle. The ultrasonic signals are processed to extract a few damage-sensitive features from the time and frequency domains. These features are then fed to an artificial neural network to identify the presence of hidden defects, i.e., defects devised on the surface of the plate not facing the probing system. We find that the noncontact inspection system and the signal processing technique enable the classification of the plate health with a success rate >75%. Bagheri and Rizzo: Guided ultrasonic wave testing of an immersed plate with hidden defects Downloaded From: http://opticalengineering.spiedigitallibrary.org/ on 08/16/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx Optical Engineering 011003-5 January 2016 • Vol. 55(1) Bagheri and Rizzo: Guided ultrasonic wave testing of an immersed plate with hidden defects Downloaded From: http://opticalengineering.spiedigitallibrary.org/ on 08/16/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx Optical Engineering 011003-7 January 2016 • Vol. 55(1) Bagheri and Rizzo: Guided ultrasonic wave testing of an immersed plate with hidden defects Downloaded From: http://opticalengineering.spiedigitallibrary.org/ on 08/16/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx Optical Engineering 011003-10 January 2016 • Vol. 55(1) Bagheri and Rizzo: Guided ultrasonic wave testing of an immersed plate with hidden defects Downloaded From: http://opticalengineering.spiedigitallibrary.org/ on 08/16/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx

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Guided Ultrasonic Wave Imaging for Immersed Plates Based on Wavelet Transform and Probabilistic Analysis

Cited by 6 publications

References 37 publications

Multiscale adjoint waveform tomography for surface and body waves

Multiscale adjoint waveform tomography for surface and body waves

Numerical Analysis of Guided Waves to Improve Damage Detection and Localization in Multilayered CFRP Panel

Guided ultrasonic wave testing of an immersed plate with hidden defects

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